scholarly journals GLOBAL OPTIMIZATION OF GRILLAGES USING SIMULATED ANNEALING AND HIGH PERFORMANCE COMPUTING

2010 ◽  
Vol 16 (1) ◽  
pp. 95-101 ◽  
Author(s):  
Dmitrij Šešok ◽  
Jonas Mockus ◽  
Rimantas Belevičius ◽  
Arnas Kačeniauskas
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
High Performance ◽  
Practical Problem ◽  
Real Life ◽  
Optimization Methods ◽  
Medium Size ◽  
Reactive Force ◽  
Standard Medium ◽  
Performance Computing

The aim is to investigate ways of increasing the efficiency of grillage optimization. Following this general aim, two well‐known optimization methods, namely the Genetic Algorithm (GA) and Simulated Annealing (SA), were compared using some standard medium size (10 and 15 piles) examples. The objective function was the maximal vertical reactive force at a support. Coordinates of piles were optimization variables. SA wins and was applied to real‐life problem (55 piles) by parallel computations performed using a powerful cluster. New element is comparison of SA with GA and application of SA to a practical problem of grillage optimization. Santrauka Straipsnio tikslas - ištirti galimus rostverkiniu pamatu optimizavimo būdus. Siekiant šio tikslo du gerai žinomi optimizavimo metodai ‐ genetiniai algoritmai ir atkaitinimo modeliavimo algoritmas ‐ buvo palyginti vidutinio dydžio (10 ir 15 poliu) pavyzdžiams išspresti. Tikslo funkcija imama didžiausia atraminI poliaus reakcija. Projektavimo kintamieji ‐ poliu koordinatIs. Atkaitinimo modeliavimo metodas laimi, todel jis buvo pritaikytas praktiniam uždaviniui (55 poliai) spresti. Spresti buvo naudojamas klasteris. Naujumas ‐ genetiniu algoritmu palyginimas su atkaitinimo modeliavimo metodu bei atkaitinimo modeliavimo metodo pritaikymas sprendžiant praktini uždavini.

scholarly journals CONSTRUCTAL DESIGN AND SIMULATED ANNEALING EMPLOYED FOR GEOMETRIC OPTIMIZATION OF A Y-SHAPED CAVITY INTRUDED INTO CONDUCTIVE WALL

2015 ◽  
Vol 14 (1) ◽  
pp. 79
Author(s):  
G. V. Gonzales ◽  
E. D. Dos Santos ◽  
L. R. Emmendorfer ◽  
L. A. Isoldi ◽  
E. S. D. Estrada ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Solid Wall ◽  
Internal Heat ◽  
Optimization Methods ◽  
Optimization Method ◽  
Internal Heat Generation ◽  
Geometric Optimization ◽  
Constructal Design ◽  
Optimal Shapes

he problem study here is concerned with the geometrical evaluation of an isothermal Y-shaped cavity intruded into conducting solid wall with internal heat generation. The cavity acts as a sink of the heat generated into the solid. The main purpose here is to minimize the maximal excess of temperature (θmax) in the solid. Constructal Design, which is based on the objective and constraints principle, is employed to evaluate the geometries of Y-shaped cavity. Meanwhile, Simulated Annealing (SA) algorithm is employed as optimization method to seek for the best shapes. To validate the SA methodology, the results obtained with SA are compared with those achieved with Genetic Algorithm (GA) and Exaustive Search (ES) in recent studies of literature. The comparison between the optimization methods (SA, GA and ES) showed that Simulated Annealing is highly effective in the search for the optimal shapes of the studied case.

scholarly journals Optimizing Virtual Private Network Design Using a New Heuristic Optimization Method

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Hongbing Lian ◽  
András Faragó
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Methods ◽  
Optimization Method ◽  
Virtual Private Network ◽  
Successful Model ◽  
Search Speed ◽  
Private Network

In virtual private network (VPN) design, the goal is to implement a logical overlay network on top of a given physical network. We model the traffic loss caused by blocking not only on isolated links, but also at the network level. A successful model that captures the considered network level phenomenon is the well-known reduced load approximation. We consider here the optimization problem of maximizing the carried traffic in the VPN. This is a hard optimization problem. To deal with it, we introduce a heuristic local search technique called landscape smoothing search (LSS). This study first describes the LSS heuristic. Then we introduce an improved version called fast landscape smoothing search (FLSS) method to overcome the slow search speed when the objective function calculation is very time consuming. We apply FLSS to VPN design optimization and compare with well-known optimization methods such as simulated annealing (SA) and genetic algorithm (GA). The FLSS achieves better results for this VPN design optimization problem than simulated annealing and genetic algorithm.

scholarly journals A COMPARISON OF SIMULATED ANNEALING, GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION IN OPTIMAL FIRST-ORDER DESIGN OF INDOOR TLS NETWORKS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 75-82 ◽  
Author(s):  
F. Jia ◽  
D. Lichti
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Network Design Problem ◽  
Design System ◽  
Swarm Optimization ◽  
First Order

The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn’t guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.

Implementing some Evolutionary Computing Methods for Determining the Optimal Parameters in the Turning Process

2015 ◽  
Vol 809-810 ◽  
pp. 902-907 ◽  
Author(s):  
Adrian Florea ◽  
Nicolae Cofaru
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Real Data ◽  
Cutting Parameters ◽  
Optimization Methods ◽  
Computing Methods ◽  
Weighted Sum ◽  
Software Application ◽  
Automotive Components ◽  
Optimal Cutting Parameters

In this paper, we comparatively present two heuristics search methods – Simulated Annealing and Weighted Sum Genetic Algorithm, in order to find optimal cutting parameters in turning operation. We consider five different constraints aiming to achieve minimum total cost of machining. We developed a customizable software application in Microsoft Visual Studio with C# source code, flexible and extensible that implements the optimization methods. The experiments are based on real data gathered from S.C. “Compa” S.A Sibiu, a company that manufactures automotive components and targets improving of product quality and reducing cost and production time. The obtained results show that, although the Weighted Sum Genetic Algorithm does not guarantee the optimality of finals solution despite of a high probability to be, it is superior to that provided by Simulated Annealing.

Testing and Analysis of Several Global Optimization Algorithms

2013 ◽  
Vol 404 ◽  
pp. 543-547 ◽  
Author(s):  
Yan Zhang ◽  
Ji Long Xie
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Simulated Annealing ◽  
Optimization Algorithms ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Research Direction ◽  
Major Research ◽  
Adaptive Simulated Annealing ◽  
Optimal Calculation

Global optimization algorithms have strong adaptability become a major research direction. Three global optimization algorithms which are Multi-Island Genetic Algorithm (MIGA), Adaptive simulated annealing (ASA) and Evolutionary Algorithms (EVOL) are adopted as the optimization policy. Tested with Zakharov function and Rastrigin function to analyze the performance of the global optimization algorithms.According to the results, it can be concluded that one should choose corresponding optimization methods for optimal calculation with specific issues, so that it can obtain the best optimal solution.

scholarly journals A Machine Learning-Genetic Algorithm (ML-GA) Approach for Rapid Optimization Using High-Performance Computing

2018 ◽  
Vol 11 (5) ◽  
pp. 291-306 ◽  
Author(s):  
Ahmed Abdul Moiz ◽  
Pinaki Pal ◽  
Daniel Probst ◽  
Yuanjiang Pei ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
High Performance Computing ◽  
High Performance ◽  
Performance Computing

Stronger trust and privacy in social networks via local cooperation1

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Krzysztof Grining ◽  
Marek Klonowski ◽  
Malgorzata Sulkowska
Keyword(s):  
Social Networks ◽  
Real World ◽  
High Performance ◽  
Preferential Attachment ◽  
Formal Analysis ◽  
Real Life ◽  
Communication Overhead ◽  
Power Law Distribution ◽  
Large Groups ◽  
Performance Computing

Abstract In our article, we present several protocols that allow to efficiently construct large groups of users based only on local relations of trust. What is more, our approach proves to need only very small computational and communication overhead. Moreover, we give guarantees that a trusted core of the network is defended, even facing a powerful adversary capable of controlling a vast majority of users. This is non-trivial property in real-life networks, as those are usually modelled using preferential attachment graphs, which are extremely prone to attacks on the hub nodes. We show that using our protocols we can achieve similar robustness as Erdős–Renyí graphs, which, on the contrary, are very resistant against attacks focused on chosen nodes. Our protocols have been tested on graphs representing real-world social networks using high performance computing due to the size of the networks. In addition for some protocols, we provided a formal analysis to prove some phenomena in random graphs following power-law distribution, which we use as a network model. Finally, we explicitly demonstrate how our approach can be used to amplify security offered by some privacy-preserving protocols. We believe however that our results can be also seen as a contribution to fundamental observation about the nature of social networks. These results may help to design protocols, whenever it is necessary to gather a big group of users in highly dynamic or even adversarial settings.

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